Taking Back Curriculum: Feminist Innovations towards Nonviolent Futures
Bibliographic record
Abstract
This paper maps key factors that activate adult stakeholders in Ontario to support curriculum pertaining to consent and non-violence in K-12 education. The paper draws from a study that used three qualitative approaches: (1) the design of an original media literacy curriculum module for Ontario youth ages 13-15; (2) curriculum assessment of the module by diverse stakeholders in Ontario K-12 education (n=20); and (3) analysis of archival documents pertaining to consent education and media literacy in Ontario, including official curriculum and media reports. Four key factors united stakeholders in supporting K-12 curriculum pertaining to consent and non-violence in Ontario. Firstly, stakeholders are intrigued by media-based pedagogies that can facilitate consent education that is “culturally relevant” (Ladson-Billings 1994; 1995) for diverse learners in Ontario. Stakeholders are also more likely to support consent and violence prevention initiatives if accompanied by professional development and teaching tools. Educator collectives and political organizing also allow for more feminist and social-justice pedagogies in the classroom, including consent education. Finally, parent councils and community groups are essential places for activism and knowledge sharing that can meet the needs of community members, while addressing stakeholders’ attitudes and behaviours that gatekeep violence prevention initiatives in education.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".